How Fraud Rings Hide in Plain Sight: A Confidence-Weighted Framework for Linkage Analysis
Abstract
Coordinated fraud rarely shows up in a single account: it spreads across devices, network addresses, payment instruments, and sessions, so each action looks unremarkable alone. This article presents a vendor-neutral approach to linkage analysis for fraud and abuse detection, built around two design constructs: the R-U-T-C framework for weighing a relationship's evidentiary strength, and an evidence ladder separating observations, associations, patterns, and decisions. It examines why connected-components clustering merges unrelated users through weak or high-degree relationships, proposes safeguards against propagating risk as inherited guilt, and outlines a production architecture with human review at its center. The framework is conceptual, illustrated with a synthetic example rather than empirically validated results, and intended for fraud engineers, security architects, graph and data scientists, and risk practitioners.
Linkage analysis connects the entities behind coordinated activity into a graph and asks whether the connections, together, form a pattern. A shared identifier is evidence of...
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